IIS: Workshop on Population Health Data Measurement, Representation, and Predictive Modeling.

该研讨会汇集了一组在卫生信息学、人口健康和计算机科学方面具有互补专业知识的科学家，以确定人口健康数据测量、表示和预测建模方面的研究挑战和机遇。尽管测量、计算和通信技术取得了巨大进步，但卫生数据测量仍依赖于传统做法（例如电话调查）。相比之下，全球数十亿人拥有移动设备，如手机和音乐播放器，这些设备包含测量传感器，并且越来越具有网络意识。如果这些装置可以适当地用于人口健康数据测量，它们可以彻底改变人口健康数据的获取和使用。然而，为了广泛使用可付诸行动的人口健康数据，需要解决许多研究挑战。讲习班是围绕可用于人口健康的数据这一愿景组织的。它涵盖了一系列广泛的问题，例如：应该记录哪些数据来衡量日常健康状况？如何最有帮助地收集这些数据？如何将传感器数据分类为可操作数据？如何判断不同来源的质量？如何挖掘和关联这些数据？如何将人口数据转化为可用的知识？如何利用这些数据开发实用的卫生系统？如何为多个用户集成多个知识来源？如何使用基于模型的推理来利用现有数据（医疗记录和临床试验）来支持定制的决策制定和改进预测模型？这些新数据对医疗质量和成本有何影响？讲习班的参加者包括卫生信息学、知识表示和推理、机器学习和数据挖掘等领域的专家。该研讨会旨在提高数据挖掘、知识表示和推理、机器学习、文本分析、人机交互、社交网络和社交媒体、语义网、决策理论等领域的研究人员对健康信息学，特别是人口数据测量、表示和预测建模方面的研究挑战和机遇的认识。它还旨在使卫生信息学、公共卫生和相关领域的研究人员更好地了解可用于开发下一代卫生信息学基础设施的最新信息学方法。研讨会的成果，包括发现信息学方面的新研究挑战和机遇，将通过研讨会报告、研讨会参与者的出版物以及通过参与研究界的后续活动的推广工作广泛传播。

The workshop brings together a group of scientists with complementary expertise in health informatics, population health, and computer science to identify the research challenges and opportunities in population health data measurement, representation, and predictive modeling. Despite great advances in measurement, computing, and communication technologies, the health data measurement relies on legacy practices (e.g., phone surveys). In contrast, billions of persons worldwide have mobile devices, such as cell phones and music players, which contain measurement sensors and are increasingly network aware. If these devices could be appropriately employed for population health data measurement, they could revolutionize the acquisition and use of population health data. However, there are many research challenges that need to be addressed in order to make widespread use of actionable population health data. The workshop is organized around a vision of actionable data for population health. It covers a broad range of questions such as: What data should be recorded to measure everyday health? How should this data be most helpfully collected? How should the sensor data be classified into actionable data? How should the diverse sources be judged for quality? How should this data be mined and correlated? How can population data be transformed into usable knowledge? How should this data be used to develop practical health systems? How can multiple knowledge sources be integrated for multiple users? How can existing data (medical records and clinical trials) be leveraged using model-based inference to support customized decision making and refine predictive models? What is the impact of this new data on health quality and cost? Workshop participants include experts in the areas of health informatics, knowledge representation and inference, machine learning and data mining. Thw workshop aims to increase the awareness of research challenges and opportunities in health informatics in general, and population data measurement, representation, and predictive modeling in particular, among researchers in data mining, knowledge representation and inference, machine learning, text analysis, human-computer interaction, social networks and social media, semantic web, decision theory. It also aims to make researchers in health informatics, public health, and related areas better aware of the state of the art informatics approaches that could be leveraged to develop the next generation health informatics infrastructure. The workshop results, including new research challenges and opportunities in discovery informatics, will be broadly disseminated through the workshop report, publications by workshop participants, and outreach efforts through follow-up activities that engage the research community.